前列腺癌风险分层的新概念。
Novel concepts for risk stratification in prostate cancer.
作者信息
Patel Keval M, Gnanapragasam Vincent J
机构信息
Cancer Research UK Cambridge Institute, University of Cambridge, UK; Academic Urology Group, University of Cambridge, UK.
Academic Urology Group, University of Cambridge, UK.
出版信息
J Clin Urol. 2016 Dec;9(2 Suppl):18-23. doi: 10.1177/2051415816673502. Epub 2016 Dec 1.
Abstract
Since Partin introduced the analysis of prostate-specific antigen, clinical T-stage and Gleason scores to estimate the risk of progression in men with localised prostate cancer, our understanding of factors that modify this risk has changed drastically. There are now multiple risk stratification tools available, including look-up tables, risk stratification/classification analyses, regression-tree analyses, nomograms and artificial neural networks. Concurrently, descriptions of novel biopsy strategies, imaging modalities and biomarkers are frequently published with the aim of improving risk stratification. With an abundance of new information available, incorporating advances into clinical practice can be confusing. This article aims to outline the major novel concepts in prostate cancer risk stratification for men with biopsy confirmed prostate cancer. We will detail which of these novel techniques and tools are likely to be adopted to aid treatment decisions and enable more accurate post-diagnosis, pretreatment risk stratification.
摘要
自从帕廷引入前列腺特异性抗原、临床T分期和 Gleason评分分析来评估局限性前列腺癌男性患者的疾病进展风险以来，我们对影响该风险的因素的理解发生了巨大变化。现在有多种风险分层工具可供使用，包括查找表、风险分层/分类分析、回归树分析、列线图和人工神经网络。同时，关于新型活检策略、成像方式和生物标志物的描述也经常发表，目的是改善风险分层。由于有大量新信息可用，将这些进展纳入临床实践可能会令人困惑。本文旨在概述经活检证实患有前列腺癌的男性患者在前列腺癌风险分层方面的主要新概念。我们将详细介绍这些新技术和工具中哪些可能会被采用，以辅助治疗决策并实现更准确的诊断后、治疗前风险分层。
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本文引用的文献
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